Notes

Blog

I'm trying out this blog as a means to express preliminary, speculative thoughts in a straightforward way.
This straightforward way may induce some inaccuracies which I kindly ask the reader to excuse.

Implementing time series multi-step ahead forecasts using recurrent neural networks in TensorFlow:

Recently I started to use recursive neural networks (RNNs) in TensorFlow (TF) for time series forecasting. Specifically, I’d like to perform multistep ahead forecasts and I was wondering how to do this (1) with RNNs in general and (2) in TF in particular. Here I summarize my insights. In particular I give a short overview over some available approaches. Furthermore, I provide code on GitHub which evaluates two simple approaches on real data.

Software

CLH: CLH stands for "Causal inference algorithm for Linear processes with Hidden confounders". This is a package containing a Matlab implementation of Algorithm 1 of our paper "Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components", called CLH_NV. The package also contains an example how to run the algorithm on simulated data. Please keep in mind that this is a very preliminary implementation for test purposes.
[Download: CLH.zip.]